Reliable localization and motion prediction is a key component for autonomous driving and advanced driver-assistance systems (ADAS). For example, one component in an autonomous vehicle and ADAS is the motion planner, which takes information about the surroundings and computes a trajectory profile to navigate towards a goal location, often in presence of moving obstacles. As another example, ADAS, such as lane-change systems, need accurate information about where other vehicles are located, both at the current time, but also for some future time.
To that end, modern vehicles sometimes include a threat assessment and/or collision avoidance systems that employ object detection sensors that are used to enable collision warning or avoidance and other active safety applications. The object detection sensors may use any of a number of technologies, such as short range radar, long range radar, cameras with image processing, laser or LiDAR, ultrasound, etc. The object detection sensors detect vehicles and other objects in the path of a host vehicle, and the application software uses the object detection information to provide warnings or take actions as appropriate. In many vehicles, the object detection sensors are integrated directly into the front bumper or other fascia of the vehicle.
However, even with information about motion of moving vehicles, the threat assessment and/or collision avoidance are difficult tasks. For example, a system described in U.S. Pat. No. 8,543,261 B2 considers the threat assessment by generating optimal vehicle states and generating a threat assessment based for those optimal states. However, compute optimal paths may be computationally prohibitive, especially in complex environments.
In U.S. 2016/0109571, the threat assessment is based on a predicted trajectory of the host vehicle using the motion dynamics of the vehicle and multiple returned scan points of detected objects and computing the risk of each detected object intersecting the predicted path of the ego vehicle. However, to predict each vehicle is computationally prohibitive when there are many detected objects in the region of interest.
Accordingly, there is a need for a system and a method that an estimate a risk posed by the motions of other vehicles in the shared environment in a computationally efficient manner.